Making forecasts from time series data has become increasingly important with the increase of data collection capabilities.The forecasting in time series is mostly done in a univariate setting where historical data in the series itself is used for further time steps. Forecasting in univariate time series is a challenging task due to unpredictable variations in the historic data. The objective of this thesis is to develop prediction methods that exhibit better performance results by alleviating the challenges in univariate time series. The methods we developed for this purpose can be divided into three groups. This thesis first proposes a novel Autoregressive Integrated Moving Average (ARIMA)-Artificial Neural Network(ANN) hybrid method that works in a more general framework. This hybrid method is then expanded to feature-based hybrid approach where statistical and structural features of time series are taken into account. Experimental results show that strategies for decomposing the original data and for combining linear and nonlinear models throughout the hybridization process are key factors in the forecasting performance of the methods. Secondly, by incorporating these findings, this thesis proposes two methods that use Empirical Mode Decomposition (EMD) technique which generates more predictable components. These methods are then enhanced with method selection algorithm which determine the most suitable method for each component. The performed experiments show that our hybrid method with EMD can be an effective way to improve predictive accuracy. These experiments additionally show that having less fluctuations in already stationary time series data leads to more accurate results in forecasting. With the motivation of these consequences, this thesis thirdly proposes another novel method which recursively employs EMD technique for those fast fluctuating components until it achieves more regular and easy-to-predict sub-components. The experiments demonstrate that the recursive algorithm outperforms the previously developed and examined methods. At the end of the thesis, the methods we developed step by step are applied for the prediction of hydropower production data and the results are compared. As a result, the Recursive EMD-based method also produces more accurate predictions for the hydropower data set.